Research hotspots and development trends of microRNA in ischemia-reperfusion: network analysis of academic journals oriented by bibliometric and visualization
Bibliographic record
Abstract
Background: Ischemia-reperfusion (IR) injury can occur in the heart, brain, liver, lung, kidney, and other important organs, and may greatly increase disease mortality. MicroRNAs (miRNAs) have a variety of functions, including regulating cell differentiation, proliferation, and apoptosis. In the past 10 years, many studies on miRNAs in IR have been conducted. This study involved a visual analysis of these studies, and a discussion of research hotspots, trends, and frontiers of this topic. Methods: A total of 1,518 articles published between 2012 and 2022 on the topic of miRNA and IR and listed in the Web of Science database were analyzed visually using CiteSpace. Cooperative networks, literature citations, and keyword co-occurrence were analyzed. Results: Of the 1,518 articles, most were published after 2018, and a rapid growth in numbers of publications was seen after 2019. Articles from China numbered the highest, followed by the United States and Canada. It has been found that many miRNAs are involved in the occurrence of IR, with various regulatory mechanisms and signaling pathways. The literature clustering generated by literature co-citation analysis and the keyword co-occurrence network showed that the previous miRNA research on IR had mainly focused on the following topics: myocardial infarction, ischemic stroke, acute kidney injury, oxidative stress, and inflammatory response. More attention has been paid to long noncoding RNA (lncRNA) and exosomes, with much exploration having been conducted in these areas. Conclusions: Although miRNA is involved in the occurrence and development of IR, as a clinical intervention target for IR, further research is still needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.109 | 0.105 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".